Study on Contents Based Fast Similarity Search of High-Dimensional Multimedia Data and Its Application

在高维多媒体数据快速相似性检索的索引技术方面，研究了空间索引结构、降维方法、检索处理算法。 提出了一种兼具传统表示结构R树和M树特点的空间索引结构MR树。 简单映射是一种适用于任何度量空间的降维映射，我们给出了它的主元选择方法。 为了处理大量的查询序列，我们可以使用一种新的批处理方法来最小化I/O开销。

As for indexing technique for fast similarity search of high-dimensional multimedia data, spatial index structures, dimension reduction methods, retrieval processing algorithms are investigated. We propose a spatial index structure named MR-tree with both features of traditional representing structures R-tree and M-tree. We also present a pivot selection method for Simple Map, which is a dimension reduction mapping applicable to any metric space. To process a large number of query series, we can use a new batch method to minimize I/O cost.